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Amazon DynamoDB vs Amazon RDS for PostgreSQL: What are the differences?

Developers describe Amazon DynamoDB as "Fully managed NoSQL database service". All data items are stored on Solid State Drives (SSDs), and are replicated across 3 Availability Zones for high availability and durability. With DynamoDB, you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use. On the other hand, Amazon RDS for PostgreSQL is detailed as "* Set up, operate, and scale PostgreSQL deployments in the cloud*". Amazon RDS manages complex and time-consuming administrative tasks such as PostgreSQL software installation and upgrades, storage management, replication for high availability and back-ups for disaster recovery. With just a few clicks in the AWS Management Console, you can deploy a PostgreSQL database with automatically configured database parameters for optimal performance. Amazon RDS for PostgreSQL database instances can be provisioned with either standard storage or Provisioned IOPS storage. Once provisioned, you can scale from 10GB to 3TB of storage and from 1,000 IOPS to 30,000 IOPS.

Amazon DynamoDB can be classified as a tool in the "NoSQL Database as a Service" category, while Amazon RDS for PostgreSQL is grouped under "PostgreSQL as a Service".

Some of the features offered by Amazon DynamoDB are:

Automated Storage Scaling – There is no limit to the amount of data you can store in a DynamoDB table, and the service automatically allocates more storage, as you store more data using the DynamoDB write APIs.

Provisioned Throughput – When creating a table, simply specify how much request capacity you require. DynamoDB allocates dedicated resources to your table to meet your performance requirements, and automatically partitions data over a sufficient number of servers to meet your request capacity. If your throughput requirements change, simply update your table's request capacity using the AWS Management Console or the Amazon DynamoDB APIs. You are still able to achieve your prior throughput levels while scaling is underway.

"Predictable performance and cost" is the top reason why over 53 developers like Amazon DynamoDB, while over 22 developers mention "Easy setup, backup, monitoring" as the leading cause for choosing Amazon RDS for PostgreSQL.

Lyft, New Relic, and Sellsuki are some of the popular companies that use Amazon DynamoDB, whereas Amazon RDS for PostgreSQL is used by Instacart, Tictail, and DSTLD. Amazon DynamoDB has a broader approval, being mentioned in 429 company stacks & 173 developers stacks; compared to Amazon RDS for PostgreSQL, which is listed in 164 company stacks and 27 developer stacks.

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What is Amazon DynamoDB?

All data items are stored on Solid State Drives (SSDs), and are replicated across 3 Availability Zones for high availability and durability. With DynamoDB, you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.

What is Amazon RDS for PostgreSQL?

Amazon RDS manages complex and time-consuming administrative tasks such as PostgreSQL software installation and upgrades, storage management, replication for high availability and back-ups for disaster recovery. With just a few clicks in the AWS Management Console, you can deploy a PostgreSQL database with automatically configured database parameters for optimal performance. Amazon RDS for PostgreSQL database instances can be provisioned with either standard storage or Provisioned IOPS storage. Once provisioned, you can scale from 10GB to 3TB of storage and from 1,000 IOPS to 30,000 IOPS.

What tools integrate with Amazon DynamoDB?

What tools integrate with Amazon RDS for PostgreSQL?

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What are some alternatives to Amazon DynamoDB and Amazon RDS for PostgreSQL?

Google Cloud Datastore

Use a managed, NoSQL, schemaless database for storing non-relational data. Cloud Datastore automatically scales as you need it and supports transactions as well as robust, SQL-like queries.

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

Amazon SimpleDB

Developers simply store and query data items via web services requests and Amazon SimpleDB does the rest. Behind the scenes, Amazon SimpleDB creates and manages multiple geographically distributed replicas of your data automatically to enable high availability and data durability. Amazon SimpleDB provides a simple web services interface to create and store multiple data sets, query your data easily, and return the results. Your data is automatically indexed, making it easy to quickly find the information that you need. There is no need to pre-define a schema or change a schema if new data is added later. And scale-out is as simple as creating new domains, rather than building out new servers.

Amazon S3

Amazon Simple Storage Service provides a fully redundant data storage infrastructure for storing and retrieving any amount of data, at any time, from anywhere on the web

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

For most of the stuff we use MySQL. We just use Amazon RDS. But for some stuff we use Amazon DynamoDB. We love DynamoDB. It's amazing. We store usage data in there, for example. I think we have close to seven or eight hundred million records in there and it's scaled like you don't even notice it. You never notice any performance degradation whatsoever. It's insane, and the last time I checked we were paying $150 bucks for that.

The good thing about AWS DynamoDB is: For the amount of traffic that I have, it is free.
It is highly-scalable, it is managed by Amazon, and it is pretty fast.

It is, again, one less thing to worry about (when compared to managing your own MongoDB elsewhere).

CloudRepo uses Amazon DynamoDB

We store customer metadata in DynamoDB. We decided to use Amazon DynamoDB because it was a fully managed, highly available solution. We didn't want to operate our own SQL server and we wanted to ensure that we built CloudRepo on high availability components so that we could pass that benefit back to our customers.